dorado-bench-v2
Oxford Nanopore basecalling with Dorado on University of Michigan HPC clusters (ARMIS2 and Great Lakes). Use when running dorado basecalling, generating SLURM jobs for basecalling, benchmarking models, optimizing GPU resources, or processing POD5 data. Captures model paths, GPU allocations, and job metadata. Integrates with ont-experiments for provenance tracking. Supports fast/hac/sup models, methylation calling, and automatic resource calculation.
When & Why to Use This Skill
This Claude skill streamlines Oxford Nanopore (ONT) basecalling workflows using Dorado on University of Michigan HPC clusters (ARMIS2 and Great Lakes). It automates complex bioinformatics tasks including SLURM job generation, GPU resource optimization, and provenance tracking, enabling researchers to efficiently process POD5 data and perform methylation calling with high reproducibility.
Use Cases
- Automated SLURM Job Generation: Quickly generate and submit optimized sbatch scripts for ARMIS2 or Great Lakes clusters, pre-configured with the correct GPU partitions and memory allocations.
- Genomic Model Benchmarking: Compare performance and accuracy across 'fast', 'hac', and 'sup' Dorado models to determine the best fit for specific sequencing projects.
- Provenance and Reproducibility Tracking: Integrate with ont-experiments to automatically capture model versions, hardware metadata, and BAM statistics for scientific audit trails.
- High-Throughput Methylation Calling: Execute advanced basecalling for 5mCG and 5hmCG modifications with automatic resource scaling and memory management.
- GPU Resource Optimization: Calculate and allocate the precise number of GPU nodes and batch sizes required for different ONT chemistry and model tiers to minimize wait times on shared HPC clusters.
| name | dorado-bench-v2 |
|---|---|
| description | Oxford Nanopore basecalling with Dorado on University of Michigan HPC clusters (ARMIS2 and Great Lakes). Use when running dorado basecalling, generating SLURM jobs for basecalling, benchmarking models, optimizing GPU resources, or processing POD5 data. Captures model paths, GPU allocations, and job metadata. Integrates with ont-experiments for provenance tracking. Supports fast/hac/sup models, methylation calling, and automatic resource calculation. |
Dorado-Bench v2 - ONT Basecalling
Basecalling toolkit for UM HPC clusters with provenance tracking.
Integration
Run through ont-experiments for provenance tracking:
ont_experiments.py run basecalling exp-abc123 --model sup --output calls.bam --json stats.json
Or standalone:
python3 dorado_basecall.py /path/to/pod5 --model sup --cluster armis2 --output calls.bam
Cluster Configurations
ARMIS2 (sigbio-a40)
partition: sigbio-a40
account: bleu1
gres: gpu:a40:1
dorado: /nfs/turbo/umms-bleu-secure/programs/dorado-1.1.1-linux-x64/bin/dorado
models: /nfs/turbo/umms-bleu-secure/programs/dorado_models
Great Lakes (gpu_mig40)
partition: gpu_mig40
account: bleu99
gres: gpu:nvidia_a100_80gb_pcie_3g.40gb:1
Model Tiers
| Tier | Accuracy | ARMIS2 Resources |
|---|---|---|
| fast | ~95% | batch=4096, mem=50G, 24h |
| hac | ~98% | batch=2048, mem=75G, 72h |
| sup | ~99% | batch=1024, mem=100G, 144h |
Options
| Option | Description |
|---|---|
--model TIER |
fast, hac, sup (default: hac) |
--version VER |
Model version (default: v5.0.0) |
--cluster |
armis2 or greatlakes |
--output FILE |
Output BAM file |
--json FILE |
Output JSON statistics |
--slurm FILE |
Generate SLURM script |
--emit-moves |
Include move table |
--modifications MOD |
Enable 5mCG_5hmCG methylation |
SLURM Generation
python3 dorado_basecall.py /path/to/pod5 \
--model sup@v5.0.0 \
--cluster armis2 \
--slurm job.sbatch
sbatch job.sbatch
Event Tracking
When run through ont-experiments, captures:
- Model name and full path
- Model tier/version/chemistry
- Batch size and device
- BAM statistics (reads, qscore, N50)
- SLURM job ID, nodes, GPUs
Methylation Calling
ont_experiments.py run basecalling exp-abc123 \
--model sup \
--modifications 5mCG_5hmCG \
--output calls_5mc.bam
Resources adjusted: memory +50%, batch size -30%